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Patent Agency Ranking
公开(公告)号：US20240378424A1
公开(公告)日：2024-11-14
申请号：US18214905
申请日：2023-06-27
Applicant: Microsoft Technology Licensing, LLC
Inventor： Praveen Kumar Bodigutla , Suman Sundaresh , Souvik Ghosh , Saurabh Gupta , Sai Krishna Bollam , Arya Ghatak Choudhury , Weiheng Qian , Jiarui Wang
IPC: G06N3/0455 , G06N3/08
Abstract: Embodiments of the disclosed technologies include configuring a first machine learning model to generate and output suggested message content based on first correlations between message content and message acceptance data, where the first machine learning model includes a first encoder-decoder model architecture, configuring a second machine learning model to generate and output message evaluation data based on second correlations between the message content and the message acceptance data, where the second machine learning model includes a second encoder-decoder model architecture, coupling an output of the first machine learning model to an input of the second machine learning model, and coupling an output of the second machine learning model to an input of the first machine learning model.
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公开(公告)号：US20240378425A1
公开(公告)日：2024-11-14
申请号：US18214939
申请日：2023-06-27
Applicant: Microsoft Technology Licensing, LLC
Inventor： Praveen Kumar Bodigutla , Suman Sundaresh , Souvik Ghosh , Saurabh Gupta , Sai Krishna Bollam , Arya Ghatak Choudhury , Weiheng Qian , Jiarui Wang
IPC: G06N3/0455 , G06N3/09
Abstract: Embodiments of the disclosed technologies include receiving first message attribute data and inputting the first message attribute data to a first machine learning model. The first machine learning model is configured to generate and output suggested message content based on first correlations between message content and message acceptance data. The first machine learning model generates a first set of message content suggestions based on the first message attribute data, and selects at least one message content suggestion from the first set of message content suggestions based on message evaluation data. Feedback data related to the selected at least one message content suggestion is received. The first machine learning model is tuned based on the feedback data. The tuned first machine learning model generates a second set of message content suggestions based on the first message attribute data.
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公开(公告)号：US20250047622A1
公开(公告)日：2025-02-06
申请号：US18487408
申请日：2023-10-16
Applicant: Microsoft Technology Licensing, LLC
Inventor： Praveen Kumar Bodigutla , Sai Krishna Bollam , Saurabh Gupta
Abstract: Embodiments of the disclosed technologies are capable of generating diverse suggested message content. The embodiments describe generating a message plan comprising attribute data and section data. The embodiments further describe inputting the message plan as a prompt to a first generative model. The first generative model is fine-tuned using a training message plan. The training message plan comprises an ordered sequence of training attribute data and training section data. The training attribute data and training section data are extracted from historic messages or generated messages. The embodiments further describe generating, by the first generative model, message content suggestions based on the attribute data and section data.
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公开(公告)号：US09836671B2
公开(公告)日：2017-12-05
申请号：US14839430
申请日：2015-08-28
Applicant: Microsoft Technology Licensing, LLC
Inventor： Jianfeng Gao , Xiaodong He , Saurabh Gupta , Geoffrey G. Zweig , Forrest Iandola , Li Deng , Hao Fang , Margaret A. Mitchell , John C. Platt , Rupesh Kumar Srivastava
CPC classification number: G06K9/6267 , G06F17/277 , G06F17/2785 , G06F17/30247 , G06F17/3069 , G06K9/00442 , G06K9/00684 , G06K9/52 , G06K9/6215 , G06K9/6272 , G06N3/02 , G06N3/0454
Abstract: Disclosed herein are technologies directed to discovering semantic similarities between images and text, which can include performing image search using a textual query, performing text search using an image as a query, and/or generating captions for images using a caption generator. A semantic similarity framework can include a caption generator and can be based on a deep multimodal similar model. The deep multimodal similarity model can receive sentences and determine the relevancy of the sentences based on similarity of text vectors generated for one or more sentences to an image vector generated for an image. The text vectors and the image vector can be mapped in a semantic space, and their relevance can be determined based at least in part on the mapping. The sentence associated with the text vector determined to be the most relevant can be output as a caption for the image.
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公开(公告)号：US20170061250A1
公开(公告)日：2017-03-02
申请号：US14839430
申请日：2015-08-28
Applicant: Microsoft Technology Licensing, LLC
Inventor： Jianfeng Gao , Xiaodong He , Saurabh Gupta , Geoffrey G. Zweig , Forrest Iandola , Li Deng , Hao Fang , Margaret A. Mitchell , John C. Platt , Rupesh Kumar Srivastava
CPC classification number: G06K9/6267 , G06F17/277 , G06F17/2785 , G06F17/30247 , G06F17/3069 , G06K9/00442 , G06K9/00684 , G06K9/52 , G06K9/6215 , G06K9/6272 , G06N3/02 , G06N3/0454
Abstract: Disclosed herein are technologies directed to discovering semantic similarities between images and text, which can include performing image search using a textual query, performing text search using an image as a query, and/or generating captions for images using a caption generator. A semantic similarity framework can include a caption generator and can be based on a deep multimodal similar model. The deep multimodal similarity model can receive sentences and determine the relevancy of the sentences based on similarity of text vectors generated for one or more sentences to an image vector generated for an image. The text vectors and the image vector can be mapped in a semantic space, and their relevance can be determined based at least in part on the mapping. The sentence associated with the text vector determined to be the most relevant can be output as a caption for the image.
Abstract translation: 这里公开的是用于发现图像和文本之间的语义相似性的技术，其可以包括使用文本查询执行图像搜索，使用图像作为查询执行文本搜索，和/或使用字幕产生器生成用于图像的标题。 语义相似性框架可以包括字幕发生器，并且可以基于深多模态相似模型。 深多模态相似性模型可以接收句子，并且基于为一个或多个句子生成的文本向量与为图像生成的图像向量的相似度来确定句子的相关性。 文本向量和图像向量可以映射到语义空间中，并且可以至少部分地基于映射来确定它们的相关性。 与确定为最相关的文本向量相关联的句子可以作为图像的标题输出。
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